Density-guided and adaptive update strategy for multi-objective particle swarm optimization

As a powerful optimization technique, multi-objective particle swarm optimization (MOPSO) has been paid more and more attention by scientists. However, in more complex problems, MOPSO faces the challenges of weak global search ability and easy-to-fall-into local optimality. To address these challeng...

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Vydané v:Journal of computational design and engineering Ročník 11; číslo 5; s. 222 - 258
Hlavní autori: Zhang, Xiaoyan, Liu, Yanmin, Song, Qian, Zhang, Yansong, Yang, Jie, Wang, Xingtao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Oxford University Press 01.10.2024
한국CDE학회
Predmet:
Ablation
Algorithms
Complexity
Density
Multiple objective analysis
Mutation
Optimization techniques
Particle swarm optimization
Statistical tests
Strategy
기계공학
density-guided Cauchy mutation
parameter update
hyper-region density estimation
particle swarm optimization
multi-objective optimization problems
evolutionary state detection
ISSN:2288-5048, 2288-4300, 2288-5048
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Popis
Shrnutí:As a powerful optimization technique, multi-objective particle swarm optimization (MOPSO) has been paid more and more attention by scientists. However, in more complex problems, MOPSO faces the challenges of weak global search ability and easy-to-fall-into local optimality. To address these challenges and obtain better solutions, people have proposed many variants. In this study, a density-guided and adaptive update strategy for multi-objective particle swarm optimization (DAMOPSO) is proposed. First, an adaptive grid is used to determine the mutation particles and guides. Then, the Cauchy mutation operator is performed for the poorly distributed particles to expand the search space of the population. Additionally, the strategy of non-dominated sorting and hyper-region density are devised for maintaining external archives, which contribute to the uniform distribution of optimal solutions. Finally, an adaptive detection strategy based on the adjustment coefficient and conversion efficiency is designed to update the flight parameters. These approaches not only speed up the convergence of algorithms, but also balance exploitation and exploration more effectively. The proposed algorithm is compared with several representative multi-objective optimization algorithms on 22 benchmark functions; meanwhile, statistical tests, ablation experiments, analysis of stability, and complexity are also performed. The experimental results demonstrate DAMOPSO is more competitive than other comparison algorithms. Graphical Abstract Graphical Abstract
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2288-5048
2288-4300
2288-5048
DOI:10.1093/jcde/qwae081